Severe Weather

Severe weather prediction, particularly of tornadoes and other convective events, is a critical area of research focused on improving forecasting accuracy and lead times to mitigate damage and enhance public safety. Current research employs advanced machine learning techniques, including hybrid models combining Kalman filters with recurrent neural networks (like BiLSTMs) and attention mechanisms, as well as support vector machines and transformer-based architectures, to analyze diverse data sources such as radar, satellite imagery, and meteorological observations. These efforts aim to improve the accuracy and timeliness of severe weather warnings, leveraging multimodal data and sophisticated reasoning capabilities to better understand complex atmospheric dynamics. The development of open-source tools and datasets further facilitates collaborative research and accelerates progress in this vital field.